Senior Data Engineer

We are building an independent ATM-network offering qualitative cash services to all individuals and merchants in Belgium. We set the goal to become the largest player on the Belgian market in the next few years.

We are looking for an experienced Data Engineer to join our team.

Your role

Batopin operates a nationwide network of CASH-points. Every site generates data: transactions and availability, incidents and maintenance interventions, cash logistics, cost, and regulatory reporting. Today that data sits in separate systems, owned by separate teams, and is read through separate lenses.

Batopin is moving to a Data 360 view: a single, coherent view of each site that allows us to manage it from every angle at once — operational performance, regulatory obligation, financial return, security and service quality. One accepted version of the truth, whichever question is being asked.

We are looking for an experienced Data Engineer to own the platform that makes this possible. You are responsible for the corporate data warehouse and for the reporting and AI environments built on top of it. You keep them running and you move them forward: run and change sit with the same person, and that person is you.

This is a senior, independent role. You will be the reference point inside Batopin for data modelling, platform design and reporting architecture, working directly with the business to turn their questions into models that hold up over time. We expect someone who has already carried a platform.

As a regulated financial institution, we operate under strict security, confidentiality and audit requirements. You will be expected to uphold these standards in everything you touch.

What will you do

Data Warehouse — Run

• Own the day-to-day operation of the corporate data warehouse on Microsoft Fabric: ingestion, pipelines, transformations, refresh schedules and data quality
• Monitor, investigate and resolve load failures, latency and reconciliation breaks; keep source-to-report lineage intact
• Manage capacity, performance and cost of the Fabric environment
• Maintain source-system integrations and adapt them when upstream systems change
• Guarantee data and reporting availability against agreed business deadlines, including monthly close and regulatory reporting cycles

Data Warehouse — Evolution

• Design and extend the dimensional model using fact and dimension concepts: star schema design, granularity decisions, slowly changing dimensions and conformed dimensions across subject areas
• Onboard new source systems and new subject areas into the warehouse
• Translate business questions into durable, reusable models rather than one-off extracts
• Manage releases through development, test and production with proper change control

Data 360

• Build the site-centric data model that allows Batopin to view every CASH-point across operations, regulatory, financial, security and service dimensions
• Establish master data and conformed dimensions — site, device, vendor, contract, time — so that figures reconcile whichever angle they are read from
• Work with Facility, Operations, Finance, Risk and Compliance to agree shared definitions and a single accepted figure per metric
• Sequence the build so that value is delivered subject area by subject area, not in one large programme

Reporting and AI Layer

• Build and maintain Power BI semantic models, reports and dashboards on the Fabric platform
• Own the semantic layer: measures, DAX, row-level security and certified datasets
• Develop and support AI and advanced analytics use cases on the platform, working with the business to identify where they genuinely add value
• Set and enforce standards for report design, naming, certification and lifecycle

Business Enablement and Self-Service

• Act as the point of contact for colleagues and teams across Batopin who want to build their own reports and run their own analyses
• Coach and train business users on Power BI and on the certified datasets, so teams become self-sufficient instead of dependent on a request queue
• Deliver certified, well-documented datasets and a clear data dictionary, so business users start from trusted data rather than rebuilding their own logic
• Hold regular working sessions with key departments to understand the questions they are trying to answer and help them get there themselves
• Set the guardrails — workspace structure, naming, certification and access — so self-service can grow without the model fragmenting into conflicting figures
• Track which questions keep coming back as ad-hoc requests, and make them answerable directly by the business

Governance, Security and Complian